






"""simple docstring"""





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class 	a		:






    def __init__(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=99			,		__SCREAMING_SNAKE_CASE	:				List[str]=13			,		__SCREAMING_SNAKE_CASE	:				str=7			,		__SCREAMING_SNAKE_CASE	:				List[Any]=9			,		__SCREAMING_SNAKE_CASE	:				int=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=True			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=False			,		__SCREAMING_SNAKE_CASE	:				str=32			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=5			,		__SCREAMING_SNAKE_CASE	:				str=4			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=37			,		__SCREAMING_SNAKE_CASE	:				Dict=8			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=0.1			,		__SCREAMING_SNAKE_CASE	:				str=0.002			,		__SCREAMING_SNAKE_CASE	:				str=1			,		__SCREAMING_SNAKE_CASE	:				int=0			,		__SCREAMING_SNAKE_CASE	:				List[str]=0			,		__SCREAMING_SNAKE_CASE	:				Dict=None			,		__SCREAMING_SNAKE_CASE	:				int=None			,		)	->						List[str]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		encoder_seq_length
      lowerCamelCase_       	=		decoder_seq_length
      # For common tests
      lowerCamelCase_       	=		self.decoder_seq_length
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_attention_mask
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		vocab_size
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		d_ff
      lowerCamelCase_       	=		relative_attention_num_buckets
      lowerCamelCase_       	=		dropout_rate
      lowerCamelCase_       	=		initializer_factor
      lowerCamelCase_       	=		eos_token_id
      lowerCamelCase_       	=		pad_token_id
      lowerCamelCase_       	=		decoder_start_token_id
      lowerCamelCase_       	=		None
      lowerCamelCase_       	=		decoder_layers






    def   UpperCamelCase			(			self	:				int   )	->						Union[str, Any]:
      return TaConfig.from_pretrained('google/umt5-base'   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				int=None			,		__SCREAMING_SNAKE_CASE	:				int=None			,		__SCREAMING_SNAKE_CASE	:				List[Any]=None			,		__SCREAMING_SNAKE_CASE	:				str=None			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=None			,		)	->						Optional[int]:
      if attention_mask is None:
        lowerCamelCase_       	=		input_ids.ne(config.pad_token_id   )
      if decoder_attention_mask is None:
        lowerCamelCase_       	=		decoder_input_ids.ne(config.pad_token_id   )
      if head_mask is None:
        lowerCamelCase_       	=		torch.ones(config.num_hidden_layers			,		config.num_attention_heads			,		device=__SCREAMING_SNAKE_CASE   )
      if decoder_head_mask is None:
        lowerCamelCase_       	=		torch.ones(config.num_decoder_layers			,		config.num_attention_heads			,		device=__SCREAMING_SNAKE_CASE   )
      if cross_attn_head_mask is None:
        lowerCamelCase_       	=		torch.ones(
            config.num_decoder_layers			,		config.num_attention_heads			,		device=__SCREAMING_SNAKE_CASE   )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      lowerCamelCase_       	=		ids_tensor([self.batch_size, self.encoder_seq_length]			,		self.vocab_size   )
      lowerCamelCase_       	=		ids_tensor([self.batch_size, self.decoder_seq_length]			,		self.vocab_size   )

      # we need to clamp the input ids here to avoid having pad token in between
      # this is because for NllbMoe the position_ids are prepared such that
      # all pad tokens have pos id = 2 and rest are between 2..seq_length
      # and the seq_length here is seq_length - num_pad_tokens
      # but when using past, there is no way of knowing if the past input ids had
      # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
      # position_ids being off by num_pad_tokens in past input
      lowerCamelCase_       	=		input_ids.clamp(self.pad_token_id + 1   )
      lowerCamelCase_       	=		decoder_input_ids.clamp(self.pad_token_id + 1   )

      lowerCamelCase_       	=		self.get_config()
      lowerCamelCase_       	=		config.num_attention_heads
      lowerCamelCase_       	=		self.prepare_inputs_dict(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      return config, input_dict






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[int]:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      return config, inputs_dict






    def   UpperCamelCase			(			self	:				Any   )	->						Tuple:
      return TaConfig(
          vocab_size=166			,		d_model=self.hidden_size			,		d_ff=self.d_ff			,		d_kv=self.hidden_size // self.num_attention_heads			,		num_layers=self.num_hidden_layers			,		num_decoder_layers=self.decoder_layers			,		num_heads=self.num_attention_heads			,		relative_attention_num_buckets=self.relative_attention_num_buckets			,		dropout_rate=self.dropout_rate			,		initializer_factor=self.initializer_factor			,		eos_token_id=self.eos_token_id			,		bos_token_id=self.pad_token_id			,		pad_token_id=self.pad_token_id			,		decoder_start_token_id=self.decoder_start_token_id			,		)






    def   UpperCamelCase			(			self	:				Any   )	->						Optional[int]:
      return TaConfig(
          vocab_size=self.vocab_size			,		d_model=self.hidden_size			,		d_ff=self.d_ff			,		d_kv=self.hidden_size // self.num_attention_heads			,		num_layers=self.num_hidden_layers			,		num_decoder_layers=self.decoder_layers			,		num_heads=self.num_attention_heads			,		relative_attention_num_buckets=self.relative_attention_num_buckets			,		dropout_rate=self.dropout_rate			,		initializer_factor=self.initializer_factor			,		eos_token_id=self.eos_token_id			,		bos_token_id=self.pad_token_id			,		pad_token_id=self.pad_token_id			,		decoder_start_token_id=self.decoder_start_token_id			,		)






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		)	->						Optional[int]:
      lowerCamelCase_       	=		UMTaModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(
          input_ids=__SCREAMING_SNAKE_CASE			,		decoder_input_ids=__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		decoder_attention_mask=__SCREAMING_SNAKE_CASE			,		)
      lowerCamelCase_       	=		model(input_ids=__SCREAMING_SNAKE_CASE			,		decoder_input_ids=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		result.last_hidden_state
      lowerCamelCase_       	=		result.past_key_values
      lowerCamelCase_       	=		result.encoder_last_hidden_state

      self.parent.assertEqual(encoder_output.size()			,		(self.batch_size, self.encoder_seq_length, self.hidden_size)   )
      self.parent.assertEqual(decoder_output.size()			,		(self.batch_size, self.decoder_seq_length, self.hidden_size)   )
      # There should be `num_layers` key value embeddings stored in decoder_past
      self.parent.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		config.num_layers   )
      # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
      self.parent.assertEqual(len(decoder_past[0]   )			,		4   )






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		)	->						Union[str, Any]:
      lowerCamelCase_       	=		UMTaModel(config=__SCREAMING_SNAKE_CASE   ).get_decoder().to(__SCREAMING_SNAKE_CASE   ).eval()
      # first forward pass
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		use_cache=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		use_cache=__SCREAMING_SNAKE_CASE   )

      self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE   ) == len(__SCREAMING_SNAKE_CASE   )   )
      self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE   ) == len(__SCREAMING_SNAKE_CASE   ) + 1   )

      lowerCamelCase_	,      lowerCamelCase_       	=		outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      lowerCamelCase_       	=		ids_tensor((self.batch_size, 1)			,		config.vocab_size   )

      # append to next input_ids and
      lowerCamelCase_       	=		torch.cat([input_ids, next_tokens]			,		dim=-1   )

      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )['last_hidden_state']
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		past_key_values=__SCREAMING_SNAKE_CASE   )['last_hidden_state']

      # select random slice
      lowerCamelCase_       	=		ids_tensor((1,)			,		output_from_past.shape[-1]   ).item()
      lowerCamelCase_       	=		output_from_no_past[:, -1, random_slice_idx].detach()
      lowerCamelCase_       	=		output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		atol=1e-3   )   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		)	->						Union[str, Any]:
      lowerCamelCase_       	=		UMTaModel(config=__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   ).half().eval()
      lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )['last_hidden_state']
      self.parent.assertFalse(torch.isnan(__SCREAMING_SNAKE_CASE   ).any().item()   )






@require_torch
class 	a		(   __snake_case    , __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    SCREAMING_SNAKE_CASE		:	Tuple		=					(UMTaForConditionalGeneration,) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	int		=					(
        {
            """conversational""": UMTaForConditionalGeneration,
            """feature-extraction""": UMTaModel,
            """summarization""": UMTaForConditionalGeneration,
            """text2text-generation""": UMTaForConditionalGeneration,
            """translation""": UMTaForConditionalGeneration,
            """question-answering""": UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					True
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					False
    SCREAMING_SNAKE_CASE		:	Tuple		=					False
    SCREAMING_SNAKE_CASE		:	Dict		=					True
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    SCREAMING_SNAKE_CASE		:	List[Any]		=					[0.8, 0.9]






    def   UpperCamelCase			(			self	:				Any   )	->						str:
      lowerCamelCase_       	=		UMTaModelTester(self   )






    @unittest.skip('Test has a segmentation fault on torch 1.8.0'   )
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						str:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      lowerCamelCase_       	=		UMTaModel(config_and_inputs[0]   ).to(__SCREAMING_SNAKE_CASE   )
      with tempfile.TemporaryDirectory() as tmpdirname:
        torch.onnx.export(
            __SCREAMING_SNAKE_CASE			,		(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,		F'''{tmpdirname}/t5_test.onnx'''			,		export_params=__SCREAMING_SNAKE_CASE			,		opset_version=9			,		input_names=['input_ids', 'decoder_input_ids']			,		)






    @unittest.skipIf(torch_device == 'cpu'			,		'Cant do half precision'   )
    def   UpperCamelCase			(			self	:				str   )	->						Optional[int]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str   )	->						int:
      lowerCamelCase_       	=		['encoder_attentions', 'decoder_attentions', 'cross_attentions']
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      lowerCamelCase_       	=		config_and_inputs[0]
      lowerCamelCase_       	=		UMTaForConditionalGeneration(__SCREAMING_SNAKE_CASE   ).eval()
      model.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'head_mask': torch.zeros(config.num_layers			,		config.num_heads			,		device=__SCREAMING_SNAKE_CASE   ),
          'decoder_head_mask': torch.zeros(config.num_decoder_layers			,		config.num_heads			,		device=__SCREAMING_SNAKE_CASE   ),
          'cross_attn_head_mask': torch.zeros(config.num_decoder_layers			,		config.num_heads			,		device=__SCREAMING_SNAKE_CASE   ),
      }

      for attn_name, (name, mask) in zip(__SCREAMING_SNAKE_CASE			,		head_masking.items()   ):
        lowerCamelCase_       	=		{name: mask}
        # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
        if name == "head_mask":
          lowerCamelCase_       	=		torch.ones(
              config.num_decoder_layers			,		config.num_heads			,		device=__SCREAMING_SNAKE_CASE   )

        lowerCamelCase_       	=		model.generate(
            config_and_inputs[1]['input_ids']			,		num_beams=1			,		max_length=3			,		output_attentions=__SCREAMING_SNAKE_CASE			,		return_dict_in_generate=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE			,		)
        # We check the state of decoder_attentions and cross_attentions just from the last step
        lowerCamelCase_       	=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
        self.assertEqual(sum([w.sum().item() for w in attn_weights]   )			,		0.0   )






    @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'   )
    def   UpperCamelCase			(			self	:				Dict   )	->						Dict:
      pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 	a		(   unittest.TestCase						):






    @slow
    @unittest.skip(
        'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'   )
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						int:
      lowerCamelCase_       	=		UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,		return_dict=__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		AutoTokenizer.from_pretrained('google/umt5-small'			,		use_fast=__SCREAMING_SNAKE_CASE			,		legacy=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		[
          'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
          'No se como puedo <extra_id_0>.',
          'This is the reason why we <extra_id_0> them.',
          'The <extra_id_0> walks in <extra_id_1>, seats',
          'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
      ]
      lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE			,		return_tensors='pt'			,		padding=__SCREAMING_SNAKE_CASE   ).input_ids
      # fmt: off
      lowerCamelCase_       	=		torch.tensor(
          [
              [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
          ]   )
      # fmt: on
      torch.testing.assert_allclose(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		model.generate(input_ids.to(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		[
          '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
          '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
      ]
      lowerCamelCase_       	=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )
      self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow






@is_pipeline_test
@require_torch
class 	a		(   unittest.TestCase						):






    @require_torch
    def   UpperCamelCase			(			self	:				str   )	->						str:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='hf-internal-testing/tiny-clap-htsat-unfused'   )
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[{'score': 0.501, 'label': 'Sound of a dog'}, {'score': 0.499, 'label': 'Sound of vaccum cleaner'}]			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      pass






    @slow
    @require_torch
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='laion/clap-htsat-unfused'			,		)
      # This is an audio of a dog
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              {'score': 0.999, 'label': 'Sound of a dog'},
              {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
          ]			,		)

      lowerCamelCase_       	=		audio_classifier([audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)
      lowerCamelCase_       	=		audio_classifier(
          [audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']			,		batch_size=5   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      pass




